Causal Decision Agent

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synth_sensitivity_plot

Read-only

Generate multi-panel diagnostic plots from sensitivity analysis results to assess how conclusions change under varying assumptions and inform rollout decisions.

Instructions

Multi-panel sensitivity diagnostic plot.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`title`	No	Super-title for the figure.
`detail`	No	Payload depth: 'minimal' (~150 tokens) for sub-step calls where only the point estimate is needed; 'standard' (~1K tokens) for diagnostics + coefficient table; 'agent' (~2K tokens, default) adds violations / next_steps / suggested_functions so the LLM can plan its next call without another round-trip.	agent
`figsize`	No	Figure size in inches.
`as_handle`	No	If true, cache the fitted result on the server and return result_id + result_uri alongside the JSON payload so a subsequent tools/call can chain without re-running.
`data_path`	No	Absolute path or URL to a data file. Supported: .csv / .tsv / .txt (delimited), .parquet / .pq, .feather / .arrow, .xlsx / .xls, .dta (Stata), .json / .jsonl. Schemes: file://, s3://, gs://, https://.
`result_id`	No	Optional handle to a previously-fitted result (returned by an earlier call when as_handle=true). Tools that operate on a fitted object accept this in place of re-supplying data_path + columns.
`data_columns`	No	Optional column projection. Parquet/Feather/Stata loaders honour this for fast partial reads.
`data_sample_n`	No	Optional uniform random subsample size (seed=0, deterministic) — useful on huge panels.
`sensitivity_result`	Yes	Output from :func:`synth_sensitivity`.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 15, 2026

TDQS

C2.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, so the read-only nature is covered. However, the description adds no behavioral detail—no mention of what the plot shows, how it is returned (figure object, file), or any side effects. It simply restates the tool's name in slightly more words.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence with no fluff, but it is under-specified rather than appropriately concise. It conveys almost no actionable information, so while it is short, it does not earn its place as a useful summary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 9 parameters, a required sensitivity_result object, and an output schema, this tool is complex, yet the description provides no context about how to invoke it, what the plot contains, or how to interpret results. It is completely inadequate for an agent to plan a call or understand expected behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage with detailed descriptions for every parameter (e.g., detail enum explains token payloads, data_path lists supported formats). The tool description itself contributes no parameter semantics, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Multi-panel sensitivity diagnostic plot' is vague and does not clearly state the tool's specific function beyond being a plot. It does not distinguish it from siblings like sensitivity_plot or synth_sensitivity, and the meaning of 'sensitivity diagnostic' is left ambiguous. The verb and resource are implied but not explicitly tied to the sensitivity analysis workflow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool versus alternatives. It does not mention that it consumes the output of synth_sensitivity, nor does it exclude other plotting tools. An agent receives no context for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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